GIS Concepts Flashcards
Adjacency
*Spatial Relationship
*are features connected (do they share a boundary)?
Contiguity
*Spatial Relationship
*Are a group of features all connected (lower 48 US states sharing boundaries in continuous fashion)
Overlap
*Spatial Relationships
*Do features share same location (contains / within; or intersect)?
Proximity
*spatial relationship
*how close are two features to each other?
Raster Data
*data type: image files (GeoTIFF, PNG, JPG)
*pixels with predefined cell size
Vector Data
*data type: defined X/Y coordinates
*point, polyline, polygon
Interpolation
Predicts raster cells values at new locations based on measurements from a collection of points
-ex: elevation, weather/climate forecasting, remote sensing-based analysis (NDVI - normalized difference vegetation index), pollution concentration
-common method: inverse distance weighted (distance between two points and uses that as weight)
Network Analysis
-analyses on network datasets
-ex: finding shortest paths and drive-time polygons, identifying closest facilities, choosing best location, finding best routes for a fleet of vehicles, determining service area (along pathways rather than radius buffers)
Spatial Autocorrelation (Global Moran’s I)
-measures based on both feature locations and feature values
-(given set of features and an associated value), evaluates whether pattern expressed is clustered, dispersed, or random
-output includes z-score and p-value to evaluate significance of Moran’s I Index
Data Distribution
example assessors:
-Median center (where data values are clustered in a polygon, not necessarily geographic center; may help determine where to place emergency services within a county)
-neighborhood summary
-spatial autocorrelation (is my data clustered?)
-clustering / hot spot analysis (where is my data clustered?)
